用深度神经网络融合物理规律，提升地震成像精度与稳定性。
Data-Driven and Theory-Guided Pseudo-Spectral Seismic Imaging Using Deep Neural Network Architectures
- 结合伪谱法与深度网络，构建数据驱动和理论引导双模型。
- 在合成及Marmousi数据上，深层区域成像误差更低，断层识别更准。
- 适合需要高精度地下结构重建的地质勘探领域使用。
全波形反演（FWI）通过多变量优化重建高分辨率地下模型，但面临求解器选择与数据不足的挑战。深度学习（DL）为数据驱动与物理模型融合提供了新路径。尽管时间域的深度学习FWI已有研究，伪谱方法在传统FWI中表现优异，却未被充分探索。本文将伪谱FWI引入深度学习框架，构建基于深度神经网络（DNN）和循环神经网络（RNN）的数据驱动与理论引导方法。理论推导后，在合成数据与Marmousi数据集上测试，并与确定性及时间域方法对比。结果表明，数据驱动的伪谱DNN在深层与逆冲区优于经典FWI，得益于其全局逼近能力；理论引导的RNN误差更小，断层识别更优；虽然DNN在速度对比恢复上更佳，但RNN在浅层与深层边缘定义与稳定性上表现更优。研究不仅提升了FWI性能，还揭示了深度学习反演的广泛应用潜力，并指明未来方向。
原文摘要 · Abstract (English)
Full Waveform Inversion (FWI) reconstructs high-resolution subsurface models via multi-variate optimization but faces challenges with solver selection and data availability. Deep Learning (DL) offers a promising alternative, bridging data-driven and physics-based methods. While FWI in DL has been explored in the time domain, the pseudo-spectral approach remains underutilized, despite its success in classical FWI. This thesis integrates pseudo-spectral FWI into DL, formulating both data-driven and theory-guided approaches using Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs). These methods were theoretically derived, tested on synthetic and Marmousi datasets, and compared with deterministic and time-domain approaches. Results show that data-driven pseudo-spectral DNNs outperform classical FWI in deeper and over-thrust regions due to their global approximation capability. Theory-guided RNNs yield greater accuracy, with lower error and better fault identification. While DNNs excel in velocity contrast recovery, RNNs provide superior edge definition and stability in shallow and deep sections. Beyond enhancing FWI performance, this research identifies broader applications of DL-based inversion and outlines future directions for these frameworks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。